GitLab

Add rules of cuda_runtime.h, cuda_driver.h and cuda_nvml.h to hip.
Signed-off-by: wenjh <wenjh@sugon.com>

Default use of hipMallocAsync rather than hipMalloc in rocblas_gemm and
add support of fp16_fp16_fp32 in rocblas_gemm.
Signed-off-by: wenjh <wenjh@sugon.com>

Fix launch params are larger than launch bounds(256) for kernels in
rocm_gemm.cu
Signed-off-by: wenjh <wenjh@sugon.com>

Signed-off-by: wenjh <wenjh@sugon.com>

[RocblasGemm] Provide support of AB(bf16)D(fp32)
Signed-off-by: wenjh <wenjh@sugon.com>

Signed-off-by: wenjh <wenjh@sugon.com>

Ref params of rmsnorm will make program corruption with 'nil' error.
Signed-off-by: wenjh <wenjh@sugon.com>

Due to the difference of warp size between nvidia(32) and dtk(64), the
OperatorTest/CTDBiasTestSuite.TestCTDBias/* are all failed except:

* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat32X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat16X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xbfloat16X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat8e5m2X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat8e4m3X65536X128

This commit is intended to fix this.
Signed-off-by: wenjh <wenjh@sugon.com>

Due to the compiler compiling incorrect code. The following test case crashed:

* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X2048X12288
* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X65536X128
* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X256X65536

This commit is intended to fix these test cases.
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* move swizzle scaling factor to cpp
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* resolve comments
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* Allow NVTEShape to own data.
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* Convert repeated copy paths to nvte_make_shape calls.
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* add support for new recipe on permute_fusion, rm fp unpermute
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Repeated calls to nvte_shape should not invalidate
previous data pointers.

It would be possible to avoid unnecessary comparisons
by duplicating some of the logic from shape() so that
the cache is only relevant when columnwise shapes are
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* Minor stylistic tweaks and typo fixes

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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
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* Update NVTE_BLOCK_SCALING for GEMM.
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
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Scale tensors are stored in a format emenable for matrix multiplication,
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Fusions of quantization and DBIAS or activation functions are not yet
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Tests for quantization are included in C++ and pytorch layers, with
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Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>